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Cited by 23 publications
(15 citation statements)
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References 23 publications
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“…Various computer based analysis techniques enabled us to assess and predict the pharmaceutical parameters through the machine learning process [ 18 ]. KNN model did not need parameter based simulation and was aimed to find the best feasible K value which satisfied the condition that the predicted value is as close as the known output value of the hold-out dataset [ 19 ]. ANN are networks of adaptable nodes that store experimental knowledge through the machine-based learning process from the given samples.…”
Section: Discussionmentioning
confidence: 99%
“…Various computer based analysis techniques enabled us to assess and predict the pharmaceutical parameters through the machine learning process [ 18 ]. KNN model did not need parameter based simulation and was aimed to find the best feasible K value which satisfied the condition that the predicted value is as close as the known output value of the hold-out dataset [ 19 ]. ANN are networks of adaptable nodes that store experimental knowledge through the machine-based learning process from the given samples.…”
Section: Discussionmentioning
confidence: 99%
“…Different authors use these features and others for classification; in an analysis done by Mustafa et al, [2] to classify mental stages through spectrograms, the authors extracted 80 statistical features for four orientations of the matrix, reducing the features vector by applying PCA, and used K nearest neighbors(KNN) to classify the stages. In [18], a comparison was made of two classifiers -Support Vector Machine and Artificial Neural Networks-following the same methodology, but the features vector had other statistical features, improving the accuracy for KNN (using Euclidian distance) in comparison with the Artificial Neural Network (ANN). A BCI system [19] based on motor imagery acts in real-time using a single channel to classify the left-and right-hand motor imagery signals; these features are texture descriptors, employing a logistic regression classifier in offline mode.…”
Section: Gray-level Co-occurrence Matrix (Glcm)mentioning
confidence: 99%
“…To illustrate the reason why KNN and BNN are preferred to be combined with each other, we give a comparison between different classification and regression methods (the result is shown in Table ). From the table, KNN is compared with ANN with feedforward propagation (Mustafa et al , ) and linear classification (LC) (Huang and Yang, ) in terms of accuracy of rainfall‐occurrence determinations and dry‐day proportion. The determination is based on December data and the regression is based on all‐year daily rains with intensities above 53.5 mm day −1 .…”
Section: Model Descriptionmentioning
confidence: 99%